Our method differs from the literature [1]}, [2]}, [3]} with its complete feature pooling assumption (i.e. efficient and effective). Similarly, in [1]}, local features are encoded on small spatio-temporal grids and temporal structure is captured by a fully connected layer. But, these models need large-scale data to train a reliable model and to find an optimum solution for the problem. On the other hand, our method can still attain moderate performance by merely encoding deep features extracted from a pretrained model while capturing short-time temporal structure. Moreover, it allows to refine the parameters of the CNN model by back-propagating error through the network for more correlated visual deep features in the problem.
